有向領域グラフのエルゴード的巡回による閉鎖空間での能動的異常検知
Active Anomaly Detection in Confined Spaces Using Ergodic Traversal of Directed Region Graphs
閉鎖空間を有向領域グラフで表現し、異常検知の不確実性に応じて領域訪問頻度を調整するエルゴード的経路計画を提案。シミュレーションで高速収束と異常検知の高信頼性を確認。
著者: Benjamin Wong, Tyler M. Paine, Santosh Devasia, Ashis G. Banerjee
分類: cs.RO
原文アブストラクト
We provide the first step toward developing a hierarchical control-estimation framework to actively plan robot trajectories for anomaly detection in confined spaces. The space is represented globally using a directed region graph, where a region is a landmark that needs to be visited (inspected). We devise a fast mixing Markov chain to find an ergodic route that traverses this graph so that the region visitation frequency is proportional to its anomaly detection uncertainty, while satisfying the edge directionality (region transition) constraint(s). Preliminary simulation results show fast convergence to the ergodic solution and confident estimation of the presence of anomalies in the inspected regions.
